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1_train.py
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1_train.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import sys
import matplotlib.pyplot as plt
import tensorflow as tf
import os
sys.path.append("../utils/")
import polygon_utils
import tf_utils
import model
import dataset
import loss
FLAGS = None
# --- Params --- #
ROOT_DIR = "../../"
# Data
input_res = 64
input_channels = 3
INPUT_DYNAMIC_RANGE = [-1, 1] # [min, max] the network expects
output_vertex_count = 4
TFRECORDS_DIR = os.path.join(ROOT_DIR, "data/photovoltaic_array_location_dataset/tfrecords.unet_and_vectorization")
# Model
# Training
LEARNING_RATE_PARAMS = {
"boundaries": [50000],
"values": [1e-4, 1e-4]
}
batch_size = 128
max_iter = 50000
correct_dist_threshold = 1 / input_res # 1px
# Validation
# The following value has to be equal to batch_size because the effective size of any batch has to be equal to
# batch_size (the custom loss function needs this condition)
dataset_val_size = 256
train_loss_accuracy_steps = 50
val_loss_accuracy_steps = 250
checkpoint_steps = 250
# Outputs
model_name = "unet-and-vectorization-photovoltaic-arrays"
LOGS_DIR = os.path.join(ROOT_DIR, "code/unet_and_vectorization/runs/current/logs")
CHECKPOINTS_DIR = os.path.join(ROOT_DIR, "code/unet_and_vectorization/runs/current/checkpoints")
# --- --- #
def init_plots():
fig1 = plt.figure(1, figsize=(5, 5))
fig1.canvas.set_window_title('Training')
fig2 = plt.figure(2, figsize=(5, 5))
fig2.canvas.set_window_title('Validation')
plt.ion()
def plot_results(figure_index, image_batch, polygon_batch, y_image_batch):
im_res = image_batch[0].shape[0]
image = (image_batch[0] - INPUT_DYNAMIC_RANGE[0]) / (INPUT_DYNAMIC_RANGE[1] - INPUT_DYNAMIC_RANGE[0])
y_image = y_image_batch[0]
train_polygon = polygon_batch[0] * im_res
plt.figure(figure_index)
plt.cla()
plt.imshow(image)
plt.imshow(y_image[:, :, 0], alpha=0.5, cmap="gray")
polygon_utils.plot_polygon(train_polygon, label_direction=1)
plt.draw()
plt.pause(0.001)
def main(_):
# Create the input placeholder
x_image = tf.placeholder(tf.float32, [batch_size, input_res, input_res, input_channels])
# Define loss and optimizer
y_image_ = tf.placeholder(tf.float32, [batch_size, input_res, input_res, 1])
y_image, mode_training = model.make_unet(x_image=x_image)
# Build the objective loss function as well as the accuracy parts of the graph
total_loss = loss.cross_entropy(y_image, y_image_)
tf.summary.scalar('total_loss', total_loss)
global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')
learning_rate = tf.train.piecewise_constant(global_step, LEARNING_RATE_PARAMS["boundaries"],
LEARNING_RATE_PARAMS["values"])
with tf.name_scope('adam_optimizer'):
train_step = tf.train.AdamOptimizer(learning_rate).minimize(total_loss, global_step=global_step)
# Summaries
merged_summaries = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(os.path.join(LOGS_DIR, "train"), tf.get_default_graph())
val_writer = tf.summary.FileWriter(os.path.join(LOGS_DIR, "val"), tf.get_default_graph())
# Dataset
train_dataset_filename = os.path.join(TFRECORDS_DIR, "train.tfrecord")
train_images, train_polygons, train_raster_polygons = dataset.read_and_decode(train_dataset_filename, input_res,
output_vertex_count, batch_size,
INPUT_DYNAMIC_RANGE)
val_dataset_filename = os.path.join(TFRECORDS_DIR, "val.tfrecord")
val_images, val_polygons, val_raster_polygons = dataset.read_and_decode(val_dataset_filename, input_res,
output_vertex_count, batch_size,
INPUT_DYNAMIC_RANGE,
augment_dataset=False)
# Savers
saver = tf.train.Saver()
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
with tf.Session() as sess:
sess.run(init_op)
# Restore checkpoint if one exists
checkpoint = tf.train.get_checkpoint_state(CHECKPOINTS_DIR)
if checkpoint and checkpoint.model_checkpoint_path: # First check if the whole model has a checkpoint
print("Restoring {} checkpoint {}".format(model_name, checkpoint.model_checkpoint_path))
saver.restore(sess, checkpoint.model_checkpoint_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
init_plots()
print("Model has {} trainable variables".format(
tf_utils.count_number_trainable_params())
)
i = tf.train.global_step(sess, global_step)
while i <= max_iter:
train_image_batch, train_polygon_batch, train_raster_polygon_batch = sess.run(
[train_images, train_polygons, train_raster_polygons])
if i % train_loss_accuracy_steps == 0:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
train_summary, _, train_loss, train_y_image = sess.run(
[merged_summaries, train_step, total_loss, y_image],
feed_dict={x_image: train_image_batch, y_image_: train_raster_polygon_batch,
mode_training: True}, options=run_options, run_metadata=run_metadata)
train_writer.add_summary(train_summary, i)
train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
print('step %d, training loss = %g' % (i, train_loss))
plot_results(1, train_image_batch, train_polygon_batch, train_y_image)
else:
_ = sess.run([train_step], feed_dict={x_image: train_image_batch, y_image_: train_raster_polygon_batch,
mode_training: True})
# Measure validation loss and accuracy
if i % val_loss_accuracy_steps == 1:
val_image_batch, val_polygon_batch, val_raster_polygon_batch = sess.run(
[val_images, val_polygons, val_raster_polygons])
val_summary, val_loss, val_y_image = sess.run(
[merged_summaries, total_loss, y_image],
feed_dict={
x_image: val_image_batch,
y_image_: val_raster_polygon_batch, mode_training: True})
val_writer.add_summary(val_summary, i)
print('step %d, validation loss = %g' % (i, val_loss))
plot_results(2, val_image_batch, val_polygon_batch, val_y_image)
# Save checkpoint
if i % checkpoint_steps == (checkpoint_steps - 1):
saver.save(sess, os.path.join(CHECKPOINTS_DIR, model_name),
global_step=global_step)
i = tf.train.global_step(sess, global_step)
coord.request_stop()
coord.join(threads)
train_writer.close()
val_writer.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', type=str,
default='/tmp/tensorflow/mnist/input_data',
help='Directory for storing input data')
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)